We evaluated the ability of numerical habitat models (NHM) to predict the distribution of juveniles of Atlantic salmon (Salmo salar) in a river. NHMs comprise a hydrodynamic model (to predict water depth and current speed for any given flow) and a biological model (to predict habitat quality for fish using water depth, current speed, and substrate composition). We implemented NHMs with a biological model based on (i) preference curves defined by the ratio of the use to the availability of physical conditions and (ii) a multivariate logistic regression that distinguished between the physical conditions used and avoided by fish. Preference curves provided a habitat suitability index (HSI) ranging from 0 to 1, and the logistic regression produced a habitat probabilistic index (HPI) representing the probability of observing a parr under given physical conditions. Pearson's correlation coefficients between HSI and local densities of parr ranged from 0.39 to 0.63 depending on flow. Corresponding values for HPI ranged from 0.81 to 0.98. We concluded that HPI may be a more powerful biological model than HSI for predicting local variations in fish density, forecasting fish distribution patterns, and performing summer habitat modelling for Atlantic salmon juveniles.

We assessed the transferability of the habitat suitability index (HSI) and the habitat probabilistic index (HPI) between two rivers. Transferability was measured by the ability of HSI and HPI models developed in the Sainte-Marguerite River to predict the distribution of Atlantic salmon parr (Salmo salar) in the Escoumins River. HSI and HPI were based on the pattern of utilization by fish of water depth, current velocity, and substrate size. HSI was developed using the preference curve approach, and HPI was developed using a multiple logistic regression. Predicted values of HSI and HPI in Escoumins River ranged from 0 (poor habitat) to 1 (excellent habitat). Fish density in habitat patches assigned different HSI or HPI values ranged from 0 to 1 fish·100 m–2. Only HPI adequately predicted local variations in parr density (r2 = 0.84) in habitat patches of Escoumins River. Our results suggest that HSI is less transferable between rivers than HPI. Differences in substrate size between the two rivers is suspected to impede the transferability of the HSI model. We also argue that the mathematical structure of HPI provides a larger degree of flexibility that facilitates its transferability and its potential generalization.

Summary The Rupert River is one of the largest tributaries on the east coast of James Bay. Lake sturgeon (Acipenser fulvescens) is present all along the main stem where several spawning grounds have been located, four of which are major spawning grounds that have been studied at km 216, 281, 290 and 362. The total number of drifting larvae was estimated with drift nets set along transverse transects at km 212, 276, and km 287 from 2007 to 2009, and at km 361 in 2008 and 2009, using a new technique, namely, a Doppler current meter to measure water velocity within transect sub‐sections corresponding to Voronoï polygons. There was a substantial, persistent difference in the number of larvae produced by the four main spawning areas. On average, the most productive site (km 276) produced over five times more larvae than the least productive site (km 361). Average estimated numbers were 41,194 at km 212, 176,840 at km 276, 106,212 at km 287, and 30,642 at km 361. Temporal variations were of much less amplitude than spatial differences. Between 2007 and 2009, interannual variations were not significant, except at km 212, despite differences in river flow during incubation and larval drift. The number of gravid females and the quality of spawning grounds would likely be the main factors influencing the total number of larvae. Vertical distribution of larvae is variable between sites and years, and shows a slight tendency for larvae to be more surface oriented. Higher flow near the surface would partly explain larger surface drifting of larvae. Transverse distribution is uneven and often associated with the location of the spawning grounds and the river flow. Given the uneven vertical and transverse distribution of larvae, an effective sampling strategy should cover the complete water column and full river width. Where depth exceeds 3 m, at least two stacked nets are recommended. In large rivers, filtering close to 1% of total river flow should result in acceptable confidence intervals, allowing a good comparison of the number of larvae in space and time.

Tax systems are complex structures that can be difficult for individuals to navigate. Understanding the way taxes are calculated and liabilities are assessed matters a lot when personal saving for retirement and education, and much of the government's social policy apparatus, are closely integrated with the tax system. This study uses a survey to measure individuals' knowledge about basic elements of the personal income tax, their perception of their own tax knowledge, and their tax-filing behaviour. One would hope that tax-literate Canadians would have a high level of knowledge of the way taxes work, and a realistic appreciation of the limits of their knowledge, and thus that they could make informed decisions, for example, when filing their tax returns. The survey data show that Canadians have good knowledge of basic tax facts but struggle when asked more complex questions regarding the progressivity of the income tax. Results were generally consistent across provinces with the notable exception of respondents in Quebec, who had higher marks on the authors' tax quiz but lower self-assessed tax knowledge. The measurement instrument employed in the study will allow for a refinement of research exploring the drivers of tax compliance as well as political attitudes toward taxes and redistribution.

No abstract

No abstract

Guay et al. (2000) evaluated the ability of a numerical habitat model (NHM) to predict the spatial distribution of juveniles of Atlantic salmon (Salmo salar) in a river. The NHM that we used consisted of a hydrodynamic model predicting the physical characteristics (current velocity, water depth, substrate composition) of habitats under any given flow and a biological model assigning an ecological value ranging from 0 (poor habitat) to 1 (excellent habitat) to habitats using their expected physical attributes as independent variables. The main objective of Guay et al. (2000) was to compare the predictions of NHM based on two biological models to the spatial distribution of fish in a river. The new biological model that we developed, the habitat probabilistic index (HPI), predicted a significantly larger fraction of the local variations of fish density (r 2 = 0.86) than the traditional habitat suitability index (HSI; r 2 = 0.39). Williams (2001) questioned several methodological and fundamental aspects of the work of Guay et al. (2000). The points raised by Williams (2001) about Guay et al. (2000) can be grouped into problems of wording, problems of sampling sufficiency, and problems of spatial scales.

No abstract